A Mathematical Formulation to Estimate the Effect of Grain Refiners on the Ultimate Tensile Strength of Al-Zn-Mg-Cu Alloys

Kurt, Halil Ibrahim; Oduncuoğlu, Murat; Kurt, Mehmet Ali

doi:10.3390/met5020836

Cited by 9 publications

(6 citation statements)

References 50 publications

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“…The effects of FSP parameters and hybrid ratio on the UTS of Al matrix (5083) hybrid composites are investigated in detail [12]. The numbers of different neurons in one hidden layer (10)(11)(12)(13)(14)(15) are used to determine the optimum model architecture. The optimal model architecture is conducted with 15 neurons.…”

Section: Potential Application Of Ann Anfis and Taguchi Approaches For Aluminum Alloys And Aluminum Matrix Compositesmentioning

confidence: 99%

“…The UTS of unrefined Al-Zn-Mg-Cu alloys and refined the alloys by Al-5Ti-1B and Al-5Zr master alloys are calculated with ANN [14]. There is no well-defined procedure to determine the optimal model structure, so the different neuron numbers in one hidden layer (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) are used with the trial and error approach. The optimal structure for this works is the 15-17-1 with logistic sigmoid transfer function.…”

Section: Potential Application Of Ann Anfis and Taguchi Approaches For Aluminum Alloys And Aluminum Matrix Compositesmentioning

confidence: 99%

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The Theoretical Overview of the Selected Optimization and Prediction Models Useful in the Design of Aluminum Alloys and Aluminum Matrix Composites

Kurt¹,

Ergül²,

Yilmaz³

et al. 2021

Advanced Aluminium Composites and Alloys

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The growing attention regarding aluminum alloy matrix composites within the aerospace, automotive, defense, and transportation industries make the development of new engineering materials with the improved mechanical properties. Currently, materials are selected because of their abilities to satisfy engineering demands high for strength-to-weight ratio, tensile strength, corrosion resistance, and workability. These properties make aluminum alloys and aluminum matrix composites (AMCs) an excellent option for various industrial applications. Soft computing methods such as the artificial neural network (ANN), adaptive-neuro fuzzy inference systems (ANFIS), and Taguchi with ANOVA are the most important approaches to solve the details of the mechanism and structure of materials. The optimal selection of variables has important effects on the final properties of the alloys and composites. The chapter presents original research papers from our works and taken from literature studies dealing with the theory of ANN, ANFIS, and Taguchi, and their applications in engineering design and manufacturing of aluminum alloys and AMCs. Also, the chapter identifies the strengths and limitations of the techniques. The ANFIS and ANN approaches stand out with wide properties, optimization, and prediction, and to solving the complex problems while the Taguchi experimental design technique provides the optimum results with fewer experiments.

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Section: Potential Application Of Ann Anfis and Taguchi Approaches For Aluminum Alloys And Aluminum Matrix Compositesmentioning

confidence: 99%

Section: Potential Application Of Ann Anfis and Taguchi Approaches For Aluminum Alloys And Aluminum Matrix Compositesmentioning

confidence: 99%

The Theoretical Overview of the Selected Optimization and Prediction Models Useful in the Design of Aluminum Alloys and Aluminum Matrix Composites

Kurt¹,

Ergül²,

Yilmaz³

et al. 2021

Advanced Aluminium Composites and Alloys

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mathematical modeling and prediction approaches have been widely used in a lot of disciplines such as engineering, biology, and medicine [7][8][9][10][11][12][13][14][15]. In this work, we used the ANN approach to reveal the influences of the confirmed cases, test number, time range, death and recovery rates of COVID-19 in the United State of America, China, and Turkey and compared the results.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Prediction of Covid-19 Epidemic in Turkey Comparing with USA and China

Ergül

Kurt

Oduncuoğlu

et al. 2021

Journal of Engineering Technology and Applied Sciences

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The aim of the study is to research and compare the influences of the confirmed cases, test number and time range on the death and recovery rates in the United State of America, China, and Turkey, and to find out the effect of the epidemic in the near future of Turkey. The modelling and prediction of effects of the day, case and test numbers of COVID-19 infection in the USA, China and Turkey are carried out using the artificial neural network approach (ANN). The system are trained and tested with the different numbers of neurons, hidden layers and activation functions to increase the reliability and accuracy of model. The proposed models have a high R2 value for China and Turkey. We can say according to the results that the measures taken by the USA are inadequate. The formulation is applied to predict the effect of Covid-19 infection in Turkey. The test number that is an important factor in detecting the cases should be increased. The results show a good fit between the observed data and those obtained by the ANN model. If the precautions are strictly followed, the case number will be decreased significantly after 160 days for Turkey according to result of the proposed model but due to the uncontrolled variables, this time may result in between 200 and 250 days.

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“…Nowadays, machine learning prompts data science and analytics to become a significant tool to find the desired causal relations in the material research [11], and results in developing a new field termed as "Materials Informatics" [12,13] in recent years. Machine learning has been rapidly used in the fields of metals [14][15][16][17][18], as well as polymers [19], semiconductors [20,21], which fully demonstrates its powerful universality.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural Network

Wang

et al. 2020

Metals

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Predicting mechanical properties of metals from big data is of great importance to materials engineering. The present work aims at applying artificial neural network (ANN) models to predict the tensile properties including yield strength (YS) and ultimate tensile strength (UTS) on austenitic stainless steel as a function of chemical composition, heat treatment and test temperature. The developed models have good prediction performance for YS and UTS, with R values over 0.93. The models were also tested to verify the reliability and accuracy in the context of metallurgical principles and other data published in the literature. In addition, the mean impact value analysis was conducted to quantitatively examine the relative significance of each input variable for the improvement of prediction performance. The trained models can be used as a guideline for the preparation and development of new austenitic stainless steels with the required tensile properties.

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A Mathematical Formulation to Estimate the Effect of Grain Refiners on the Ultimate Tensile Strength of Al-Zn-Mg-Cu Alloys

Cited by 9 publications

References 50 publications

The Theoretical Overview of the Selected Optimization and Prediction Models Useful in the Design of Aluminum Alloys and Aluminum Matrix Composites

The Theoretical Overview of the Selected Optimization and Prediction Models Useful in the Design of Aluminum Alloys and Aluminum Matrix Composites

Modelling and Prediction of Covid-19 Epidemic in Turkey Comparing with USA and China

Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural Network

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